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Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
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作者 Jonathan Montomoli Luca Romeo +14 位作者 Sara Moccia Michele Bernardini lucia migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators 《Journal of Intensive Medicine》 2021年第2期110-116,共7页
Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient surviv... Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival probability.Machine learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.Methods:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)registry.We applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU admission.The model was iteratively cross-validated in different subsets of the study cohort.Results:The final study population consisted of 675 patients.The XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)patients.The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources. 展开更多
关键词 Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score
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